GLM-4.5V vs Qwen3 VL 8B Thinking
Comparing GLM-4.5V and Qwen3 VL 8B Thinking across benchmarks, pricing, and capabilities.
Zhipu AI · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
GLM-4.5V and Qwen3 VL 8B Thinking trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.
On price, Qwen3 VL 8B Thinking is roughly 1.5x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Qwen3 VL 8B Thinking also accepts a larger context window (262,144 input tokens), making it the stronger choice for long documents and large codebases.
Based on current benchmark, pricing, and model metadata for 2026.
Choose GLM-4.5V
- you want predictable pricing at $0.55/M input and $2.19/M output
Choose Qwen3 VL 8B Thinking
- cost matters — it's about 1.5x cheaper per token
- you process long inputs — it offers a 262,144 token context window
- you want the most recent training data — it shipped Sep 2025
At a glance
The differences that matter most.
Performance Benchmarks
Comparative analysis across standard metrics
GLM-4.5V and Qwen3 VL 8B Thinkingdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Arena Performance
Playground indexes and blind preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, GLM-4.5V ($0.55/1M tokens) is 3.1x more expensive than Qwen3 VL 8B Thinking ($0.18/1M tokens).
For output processing, GLM-4.5V ($2.19/1M tokens) is 1.0x more expensive than Qwen3 VL 8B Thinking ($2.09/1M tokens).
In conclusion, GLM-4.5V is more expensive than Qwen3 VL 8B Thinking.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
GLM-4.5V has 99.0B more parameters than Qwen3 VL 8B Thinking, making it 1100.0% larger.
Context Window
Maximum input and output token capacity
Qwen3 VL 8B Thinking accepts 262,144 input tokens compared to GLM-4.5V's 131,072 tokens. Qwen3 VL 8B Thinking can generate longer responses up to 262,144 tokens, while GLM-4.5V is limited to 131,072 tokens.
Input Capabilities
Supported data types and modalities
Both GLM-4.5V and Qwen3 VL 8B Thinking support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
GLM-4.5V
Qwen3 VL 8B Thinking
License
Usage and distribution terms
GLM-4.5V is licensed under MIT, while Qwen3 VL 8B Thinking uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
GLM-4.5V was released on 2025-08-11, while Qwen3 VL 8B Thinking was released on 2025-09-22.
Qwen3 VL 8B Thinking is 1 month newer than GLM-4.5V.
Aug 11, 2025
1.0 years ago
Sep 22, 2025
11 months ago
1mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
GLM-4.5V is available from Fireworks, Novita. Qwen3 VL 8B Thinking is available from DeepInfra.
GLM-4.5V
Qwen3 VL 8B Thinking
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-4.5V and Qwen3 VL 8B Thinking side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-4.5V vs Qwen3 VL 8B Thinking.